Table of Contents
Advances in Artificial Neural Systems
Volume 2009, Article ID 193139, 8 pages
http://dx.doi.org/10.1155/2009/193139
Research Article

Predicting Carbonation Depth of Prestressed Concrete under Different Stress States Using Artificial Neural Network

Department of Civil Engineering, Jiangsu University, Zhenjiang 212013, China

Received 17 February 2009; Revised 7 October 2009; Accepted 25 November 2009

Academic Editor: Alfredo Weitzenfeld

Copyright © 2009 Chunhua Lu and Ronggui Liu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. W. Podolny, “Corrosion of prestressing steels and its mitigation,” Journal of the Precast/Prestressed Concrete Institute, vol. 37, no. 5, pp. 34–54, 1992. View at Google Scholar
  2. T. Adrian and P. E. Ciolko, “Corrosion and prestressed concrete bridges,” in Proceedings of the Structures Congress of the Forensic Engineering Symposium, New York, NY, USA, April 2005.
  3. C.-F. Chang and J.-W. Chen, “The experimental investigation of concrete carbonation depth,” Cement and Concrete Research, vol. 36, no. 9, pp. 1760–1767, 2006. View at Publisher · View at Google Scholar · View at Scopus
  4. K. Sisomphon and L. Franke, “Carbonation rates of concretes containing high volume of pozzolanic materials,” Cement and Concrete Research, vol. 37, no. 12, pp. 1647–1653, 2007. View at Publisher · View at Google Scholar · View at Scopus
  5. V. G. Papadakis, “Effect of supplementary cementing materials on concrete resistance against carbonation and chloride ingress,” Cement and Concrete Research, vol. 30, no. 2, pp. 291–299, 2000. View at Publisher · View at Google Scholar · View at Scopus
  6. CEB Bulletin 238, “New Approach to Durability Design—An Example for carbonation induced corrosion,” Lausanne, Switzerland, 1997.
  7. Y. F. Houst and F. H. Wittmann, “Depth profiles of carbonates formed during natural carbonation,” Cement and Concrete Research, vol. 32, no. 12, pp. 1923–1930, 2002. View at Publisher · View at Google Scholar · View at Scopus
  8. A. Steffens, D. Dinkler, and H. Ahrens, “Modeling carbonation for corrosion risk prediction of concrete structures,” Cement and Concrete Research, vol. 32, no. 6, pp. 935–941, 2002. View at Publisher · View at Google Scholar · View at Scopus
  9. A. V. Saetta and R. V. Vitaliani, “Experimental investigation and numerical modeling of carbonation process in reinforced concrete structures—part I: theoretical formulation,” Cement and Concrete Research, vol. 34, no. 4, pp. 571–579, 2004. View at Publisher · View at Google Scholar · View at Scopus
  10. C. A. Jeyasehar and K. Sumangala, “Damage assessment of prestressed concrete beams using artificial neural network (ANN) approach,” Computers and Structures, vol. 84, no. 26-27, pp. 1709–1718, 2006. View at Publisher · View at Google Scholar · View at Scopus
  11. M. Y. Rafiq, G. Bugmann, and D. J. Easterbrook, “Neural network design for engineering applications,” Computers and Structures, vol. 79, no. 17, pp. 1541–1552, 2001. View at Publisher · View at Google Scholar · View at Scopus
  12. T. Parthiban, R. Ravi, G. T. Parthiban, S. Srinivasan, K. R. Ramakrishnan, and M. Raghavan, “Neural network analysis for corrosion of steel in concrete,” Corrosion Science, vol. 47, no. 7, pp. 1625–1642, 2005. View at Publisher · View at Google Scholar · View at Scopus
  13. J. Peng, Z. Li, and B. Ma, “Neural network analysis of chloride diffusion in concrete,” Journal of Materials in Civil Engineering, vol. 14, no. 4, pp. 327–333, 2002. View at Publisher · View at Google Scholar · View at Scopus
  14. M. A. Kewalramani and R. Gupta, “Concrete compressive strength prediction using ultrasonic pulse velocity through artificial neural networks,” Automation in Construction, vol. 15, no. 3, pp. 374–379, 2006. View at Publisher · View at Google Scholar · View at Scopus
  15. JGJ/T55-96, “Technical standard of mix design for ordinary concrete,” Chinese Standard, 1996.
  16. GB50010-2002, “Code for design of concrete structures,” Chinese Standard, 2002.
  17. GBJ82-85, “Test methods of long tern performance and durable performance for ordinary concrete,” Chinese Standard, 1985.
  18. N. Jiang, Z. Zhao, and L. Ren, “Design of structural modular neural networks with genetic algorithm,” Advances in Engineering Software, vol. 34, no. 1, pp. 17–24, 2003. View at Publisher · View at Google Scholar · View at Scopus
  19. S. Rajasekaran and R. Amalraj, “Predictions of design parameters in civil engineering problems using SLNN with a single hidden RBF neuron,” Computers and Structures, vol. 80, no. 31, pp. 2495–2505, 2002. View at Publisher · View at Google Scholar · View at Scopus
  20. R. Francois and J. C. Maso, “Effect of damage in reinforced concrete on carbonation or chloride penetration,” Cement and Concrete Research, vol. 18, no. 6, pp. 961–970, 1988. View at Google Scholar · View at Scopus
  21. K. Sisomphon and L. Franke, “Carbonation rates of concretes containing high volume of pozzolanic materials,” Cement and Concrete Research, vol. 37, no. 12, pp. 1647–1653, 2007. View at Publisher · View at Google Scholar · View at Scopus